The utilization of different AI methods-based satellite communications: A survey

The potential for service continuity across uncovered and under-covered regions, service ubiquity, and service scalability are all offered by Satellite Communication (SC) for the recent decade. However, to take advantage of these benefits, it is necessary to address several obstacles first. This is because the management of resource, control and security of network, the efficient utilization of spectrum, and efficient utilization of satellite-based communication networks are more difficult to accomplish than those of Terrestrial Networks (TNs). Thereby, Artificial Intelligence (AI), which encompasses Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL), has been continuously expanding as an area of study and has shown good outcomes in a variety of applications, including wireless communication. As a consequence of this, several publications put forth a variety of algorithmic solutions to the problem involving the management of various applications for SCs. Such an algorithm will employ various DL methods to improve the effectiveness of SCs. As a consequence of this, the aim of this paper is to provide an exhaustive overview of the utilization of various AI methods for a broad aspect of SCs. Following this, a general overview of the most recent details related to various AI methods and their related classifications will be presented. In addition, the study provides a summary of the many bodies of research that apply AI-based approaches to the resolution of satellite-related problems. In addition to this, AI may also be used to spot trends in satellite traffic and utilize bandwidth more effectively, making better use of the resources.